A studio built around trustworthy ML.

The approach

The studio is led by a PhD researcher with industry experience — a combination that's genuinely rare in freelance ML. Academic rigor (honest evaluation, calibration, careful baselines) paired with the pragmatism of shipping systems that have to work in production. That background is the difference between a model that benchmarks well and one you can actually trust.

Most ML projects don't fail at the model — they fail at the boundary between the model and the real world: messy data, drifting distributions, and predictions that look confident but shouldn't. MaineFrame Labs is built around that boundary.

Work here pairs strong modeling fundamentals with a data-centric mindset: active learning to spend labeling budget where it counts, and uncertainty quantification to know which predictions are safe to act on. The output is not just a model, but a reproducible, observable system you can trust in production.

Engagements are typically freelance and project-based, with the option to embed alongside an in-house team when that fits better.

Focus areas

• Computer vision — detection & segmentation, incl. satellite / remote-sensing imagery.
• Multimodal generative AI — building with VLMs and studying their limits.
• Active learning — efficient labeling loops for scarce-label domains.
• Uncertainty quantification — calibration, UQ, and knowing when not to trust a model.
• Evaluation & explainability — honest assessment of what models actually do.